Versions

V20 - Using SJC disease designations for cohort assignment

V19 - updated to survey_data.2020-07-08_17.17.10.txt, then survey_data.2020-07-14_11.31.11.txt

V18 - updated to survey_data.2020-07-02_11.01.36.txt

V17 - Change sample to dataset; change project to cohort

V16 - use namer on chunks

V15 - change correlation plots to small dots

V14 - combine plots

V13 - change gene count threshold from categporical to numeric

V12 - exclude datasets with fewer than 100 measured genes and use pre-generated survey_data

V11 - color consistency

V10 - calculate non genic and all exonic counts for correlations analysis

V9 - add gene count; import readdist.txt for more nuanced comparison of cor with gene count

Overview

reference range for unmapped reads is calculated based on unmapped reads/total

reference range for duplicate reads is calculated based on duplicate reads/mapped reads where mapped reads = total mapped and multi-mapped (<20x) reads)

reference range for non-exonic reads is calculated based on non-exonic/non duplicate reads) where non duplicate reads = exonic and non-exonic non dupe reads)

The composition fractions are not dependent on higher level ones

Libraries

library(tidyverse)

## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✓ ggplot2 3.3.0     ✓ purrr   0.3.3
## ✓ tibble  3.0.1     ✓ dplyr   0.8.5
## ✓ tidyr   1.0.0     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.4.0

## Warning: package 'tibble' was built under R version 3.6.2

## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(janitor)

## 
## Attaching package: 'janitor'

## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test

library(viridis)

## Loading required package: viridisLite

library(knitr)
library(ggthemes) # for theme_linedraw() 
library(ggpubr) # for stat_cor

## Loading required package: magrittr

## 
## Attaching package: 'magrittr'

## The following object is masked from 'package:purrr':
## 
##     set_names

## The following object is masked from 'package:tidyr':
## 
##     extract

library(cowplot) # for ggdraw and draw_label

## 
## ********************************************************

## Note: As of version 1.0.0, cowplot does not change the

##   default ggplot2 theme anymore. To recover the previous

##   behavior, execute:
##   theme_set(theme_cowplot())

## ********************************************************

## 
## Attaching package: 'cowplot'

## The following object is masked from 'package:ggpubr':
## 
##     get_legend

## The following object is masked from 'package:ggthemes':
## 
##     theme_map

library(RColorBrewer)
library(pander)
library(kableExtra)

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

Config

inputs_dir <- "inputs"
data_file <- tibble(files = list.files(inputs_dir)) %>%
  arrange(desc(files)) %>%
  top_n(1, wt = files) %>%
  pull(files)

print(data_file)

## [1] "survey_data.2020-07-15_18.51.47.txt"

Settings

min_genes_gt_0 <- 100

Functions

is_outlier <- function(x) {
  (x > quantile(x, 0.75) + 1.5*IQR(x)) |
    (x < quantile(x, 0.25) - 1.5*IQR(x))
}


most_common_val_w_percent <- function(value_vector = c("a", "a", "b")){
  mc_vals <- tabyl(value_vector) %>%
    arrange(desc(n)) %>%
    top_n(1, n) 
  paste0(round(100*mc_vals$percent), "% ", mc_vals$value_vector)
}

# StatBin2 allows depiction of empty bins as blank instead of a horizontal line:
# https://stackoverflow.com/questions/57128090/remove-baseline-color-for-geom-histogram
StatBin2 <- ggproto(
  "StatBin2", 
  StatBin,
  compute_group = function (data, scales, binwidth = NULL, bins = NULL, 
                            center = NULL, boundary = NULL, 
                            closed = c("right", "left"), pad = FALSE, 
                            breaks = NULL, origin = NULL, right = NULL, 
                            drop = NULL, width = NULL) {
    if (!is.null(breaks)) {
      if (!scales$x$is_discrete()) {
        breaks <- scales$x$transform(breaks)
      }
      bins <- ggplot2:::bin_breaks(breaks, closed)
    }
    else if (!is.null(binwidth)) {
      if (is.function(binwidth)) {
        binwidth <- binwidth(data$x)
      }
      bins <- ggplot2:::bin_breaks_width(scales$x$dimension(), binwidth, 
                                         center = center, boundary = boundary, 
                                         closed = closed)
    }
    else {
      bins <- ggplot2:::bin_breaks_bins(scales$x$dimension(), bins, 
                                        center = center, boundary = boundary, 
                                        closed = closed)
    }
    res <- ggplot2:::bin_vector(data$x, bins, weight = data$weight, pad = pad)

    # drop 0-count bins completely before returning the dataframe
    res <- res[res$count > 0, ] 

    res
  })

Import data

col_spec <- cols(
  .default = col_double(),
  dataset_id = col_character(),
  disease = col_character(),
  pedaya = col_character(),
  gender = col_character(),
  cohort_name = col_character(),
  cohort_code = col_character(),
  doi_link = col_character(),
  source_repository = col_character(),
  repository_cohort_accession = col_character(),
  repository_dataset_accession = col_character()
)

counts_and_meta <- read_tsv(file.path(inputs_dir, data_file), col_types = col_spec) %>%
  group_by(cohort_code) %>%
  mutate(n_in_cohort = n())

Colors

# brewer.pal(8, "Paired")

these_colors <- c("#A6CEE3", "#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99", "#E31A1C", 
"#FDBF6F", "#FF7F00")

# light blue, dark blue, etc: green, red, orange (then purple and brown)

these_colors <- c("#1F78B4", "#1F78B4", "#33A02C", "#33A02C", "#E31A1C", "#E31A1C", 
"#FF7F00", "#FF7F00")

names(these_colors) <-  c("not assigned", "Total reads", "Non-exonic reads",  "Exonic reads", "Duplicate reads",  "Non-duplicate reads",  "Unmapped reads", "Mapped reads")

these_colors_with_MEND = c(these_colors, "MEND reads"="#000000")

Datasets analyzed

nrow(counts_and_meta)

## [1] 2179

Diseases in dataset

counts_and_meta %>% tabyl(disease)

##                                     disease   n      percent
##                acute lymphoblastic leukemia 680 0.3120697568
##             acute megakaryoblastic leukemia 103 0.0472693896
##                      acute myeloid leukemia 221 0.1014226709
##             acute undifferentiated leukemia   1 0.0004589261
##                      adrenocortical adenoma   1 0.0004589261
##                       adrenocortical cancer   2 0.0009178522
##                    adrenocortical carcinoma  20 0.0091785223
##                   alveolar rhabdomyosarcoma  40 0.0183570445
##                          cholangiocarcinoma   1 0.0004589261
##                    choroid plexus carcinoma  25 0.0114731528
##         desmoplastic small round cell tumor   4 0.0018357045
##      dysembryoplastic neuroepithelial tumor  13 0.0059660395
##                  embryonal rhabdomyosarcoma  42 0.0192748967
##  embryonal tumor with multilayered rosettes   1 0.0004589261
##                                  ependymoma  98 0.0449747591
##                         epithelioid sarcoma   1 0.0004589261
##                               Ewing sarcoma  70 0.0321248279
##      fibrolamellar hepatocellular carcinoma   3 0.0013767783
##                                fibromatosis   1 0.0004589261
##              gastrointestinal stromal tumor   4 0.0018357045
##                             germ cell tumor   1 0.0004589261
##                     glioblastoma multiforme  29 0.0133088573
##                                      glioma 193 0.0885727398
##                              hepatoblastoma   1 0.0004589261
##                    hepatocellular carcinoma   3 0.0013767783
##                 hypereosinophillic syndrome   1 0.0004589261
##            juvenile myelomonocytic leukemia   1 0.0004589261
##                                    leukemia   1 0.0004589261
##                                    lymphoma  49 0.0224873795
##                             medulloblastoma 201 0.0922441487
##                                    melanoma   7 0.0032124828
##             melanotic neuroectodermal tumor   1 0.0004589261
##                                  meningioma   1 0.0004589261
##                             myofibromatosis   2 0.0009178522
##                    nasopharyngeal carcinoma   2 0.0009178522
##                               neuroblastoma  12 0.0055071134
##                    neuroendocrine carcinoma   1 0.0004589261
##                                not reported   2 0.0009178522
##                not reported - QC fail by PI   1 0.0004589261
##                                osteosarcoma 157 0.0720513997
##           ovarian serous cystadenocarcinoma   1 0.0004589261
##                                      PEComa   1 0.0004589261
##                    pleuropulmonary blastoma   1 0.0004589261
##                              retinoblastoma   2 0.0009178522
##                              rhabdoid tumor  65 0.0298301973
##                            rhabdomyosarcoma  53 0.0243230840
##    spindle cell/sclerosing rhabdomyosarcoma   3 0.0013767783
##          supratentorial embryonal tumor NOS  18 0.0082606700
##                            synovial sarcoma  22 0.0100963745
##                undifferentiated sarcoma NOS   3 0.0013767783
##       undifferentiated spindle cell sarcoma   2 0.0009178522
##                                 wilms tumor  11 0.0050481872

disease_freq_table <- counts_and_meta$disease %>%
  str_to_sentence %>%
  str_replace(" nos$", " NOS") %>%
  as.factor %>%
  fct_infreq %>%
  fct_lump(prop=0.01) %>%
  tabyl (var1 = "Disease") %>%
  adorn_pct_formatting(digits = 1)

colnames(disease_freq_table)[1]="Disease"

write_tsv(disease_freq_table, "results/disease_freq_table.txt")

disease_freq_table  %>%
  kable %>% 
  kable_styling(bootstrap_options = "striped", full_width = F)

Disease	n	percent
Acute lymphoblastic leukemia	680	31.2%
Acute myeloid leukemia	221	10.1%
Medulloblastoma	201	9.2%
Glioma	193	8.9%
Osteosarcoma	157	7.2%
Acute megakaryoblastic leukemia	103	4.7%
Ependymoma	98	4.5%
Ewing sarcoma	70	3.2%
Rhabdoid tumor	65	3.0%
Rhabdomyosarcoma	53	2.4%
Lymphoma	49	2.2%
Embryonal rhabdomyosarcoma	42	1.9%
Alveolar rhabdomyosarcoma	40	1.8%
Glioblastoma multiforme	29	1.3%
Choroid plexus carcinoma	25	1.1%
Synovial sarcoma	22	1.0%
Other	131	6.0%

# many are leukemias
table(str_detect(counts_and_meta$disease, "leukemia"))

## 
## FALSE  TRUE 
##  1172  1007

Cohorts in dataset

library(ggthemes)

old_cohort_size_histogram <- ggplot(counts_and_meta) + 
  geom_histogram(aes(n_in_cohort, group=cohort_code), 
                 fill = "lightgrey", color = "black", stat = StatBin2) +
  theme_minimal(base_size = 20) +
  theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +  xlab("Cohort size") +
  ylab("Cohorts") + 
  theme(legend.position="none")

cohort_size_histogram <- ggplot(counts_and_meta %>%
  select(cohort_code, n_in_cohort) %>%
  distinct ) + 
  geom_histogram(aes(n_in_cohort), 
                 fill = "lightgrey", color = "black", stat = StatBin2,
                 breaks = seq(0,500, by=10)) +
  theme_minimal(base_size = 20) +
  theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +  
  xlab("Cohort size") +
  scale_y_continuous("Cohorts", breaks = seq(0, 8, 2)) + 
  theme(legend.position="none")

cohort_size_histogram

# for cohorts that are more than 75% one disease, color by that disease

n_distinct(counts_and_meta$cohort_code)

## [1] 48

sort(unique(counts_and_meta$n_in_cohort))

##  [1]   3   4   6   7   8  10  15  17  19  22  23  24  25  26  31  35  39
## [18]  43  44  54  56  62  63  65  78  82  84  88  89 103 127 137 141 337

counts_and_meta %>%
  select(cohort_code, n_in_cohort) %>%
  distinct %>%
  arrange(desc(n_in_cohort)) %>%
  kable

cohort_code	n_in_cohort
Cohort_1	337
Cohort_2	141
Cohort_3	137
Cohort_4	127
Cohort_5	103
Cohort_6	89
Cohort_7	88
Cohort_8	84
Cohort_9	82
Cohort_10	78
Cohort_11	65
Cohort_12	63
Cohort_13	62
Cohort_14	56
Cohort_15	54
Cohort_16	44
Cohort_17	43
Cohort_18	39
Cohort_19	35
Cohort_20	31
Cohort_21	31
Cohort_22	26
Cohort_23	25
Cohort_24	25
Cohort_26	24
Cohort_25	24
Cohort_30	23
Cohort_27	23
Cohort_28	23
Cohort_29	23
Cohort_31	22
Cohort_32	19
Cohort_33	19
Cohort_34	17
Cohort_35	15
Cohort_36	10
Cohort_37	8
Cohort_38	8
Cohort_41	7
Cohort_39	7
Cohort_40	7
Cohort_42	6
Cohort_44	6
Cohort_43	6
Cohort_45	6
Cohort_47	4
Cohort_46	4
Cohort_48	3

counts_and_meta %>%
  select(cohort_code, n_in_cohort) %>%
  distinct %>%
  pull(n_in_cohort) %>%
  median

## [1] 24.5

repository supp table

repo_info <- read_tsv("inputs/repos.tsv")

## Parsed with column specification:
## cols(
##   repo_name = col_character(),
##   repo_abbrev = col_character(),
##   repo_url = col_character(),
##   source_accession_pattern = col_character()
## )

repo_info %>%
  rename(`Name`=repo_name, `Abbreviation`=repo_abbrev, URL = repo_url) %>%
  select(-source_accession_pattern) %>%
  kable %>% 
  kable_styling(bootstrap_options = "striped", full_width = F)

Name	Abbreviation	URL
Short Read Archive	SRA	https://www.ncbi.nlm.nih.gov/sra
European Genome-phenome Archive	EGA	https://www.ebi.ac.uk/ega/home
St. Jude Cloud	SJC	https://www.stjude.cloud/
Kids First Data Portal	KFDP	https://portal.kidsfirstdrc.org/
Database of Genotypes and Phenotypes	dbGaP	https://www.ncbi.nlm.nih.gov/gap/

data access table

counts_and_meta %>% 
  select(cohort_code, cohort_name, doi_link, source_repository, repository_cohort_accession) %>%
#  group_by(across(c(-disease)))
  distinct %>%
  kable

cohort_code	cohort_name	doi_link	source_repository	repository_cohort_accession
Cohort_1	TARGET-10	NA	dbGaP	phs000218
Cohort_3	TARGET-20	NA	dbGaP	phs000218
Cohort_30	TARGET-21	NA	dbGaP	phs000218
Cohort_41	TARGET-30	NA	dbGaP	phs000218
Cohort_7	TARGET-40	NA	dbGaP	phs000218.v15.p5
Cohort_7	TARGET-40	NA	dbGaP	phs000218
Cohort_47	TARGET-50	NA	dbGaP	phs000218
Cohort_11	TARGET-52	NA	dbGaP	phs000218
Cohort_12	EGAD00001001098	http://dx.doi.org/10.1038/ng.3230	EGA	EGAD00001001098
Cohort_27	EGAD00001000356	http://dx.doi.org/10.1016/j.cell.2013.09.042	EGA	EGAD00001000356
Cohort_32	EGAD00001002680	http://dx.doi.org/10.1038/ncomms7302	EGA	EGAD00001002680
Cohort_23	EGAD00001001666	http://dx.doi.org/10.1007/s00401-016-1539-z	EGA	EGAD00001001666
Cohort_26	phs000900.v1.p1	http://dx.doi.org/10.1038/nm.3855	dbGaP	phs000900.v1.p1
Cohort_20	10.24370/SD_BHJXBDQK	http://dx.doi.org/10.24370/SD_BHJXBDQK	KFDB	CBTTC-HGG-PA-01
Cohort_20	10.24370/SD_BHJXBDQK	http://dx.doi.org/10.24370/SD_BHJXBDQK	KFDB	CBTTC_PNET_PA_01
Cohort_20	10.24370/SD_BHJXBDQK	http://dx.doi.org/10.24370/SD_BHJXBDQK	KFDB	CBTTC-PolyA
Cohort_19	phs000699.v1.p1	http://dx.doi.org/10.1073/pnas.1419260111	dbGaP	phs000699.v1.p1
Cohort_34	EGAD00001000158	http://dx.doi.org/10.1038/nature11327	EGA	EGAD00001000158
Cohort_42	EGAD00001000328	http://dx.doi.org/10.1038/nature11284	EGA	EGAD00001000328
Cohort_28	EGAD00001000617	http://dx.doi.org/10.1038/ng.2682, http://dx.doi.org/10.1016/j.cell.2013.09.042	EGA	EGAD00001000617
Cohort_18	EGAD00001001620	http://dx.doi.org/10.1016/j.cell.2013.09.042, http://dx.doi.org/10.1038/nature11284	EGA	EGAD00001001620
Cohort_25	EGAD00001000648	http://dx.doi.org/10.1016/j.cell.2013.09.042	EGA	EGAD00001000648
Cohort_46	SRP006575	No publication	SRA	SRP006575
Cohort_44	SJC_Other	https://doi.org/10.1038/ncomms7302	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_5	SJC_AMLM	https://doi.org/10.1016/j.ccr.2012.10.007	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_5	SJC_AMLM	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_2	SJC_BALL	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_2	SJC_BALL	https://doi.org/10.1056/NEJMoa1403088	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_16	SJC_CBF	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_24	SJC_CPC	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_37	SJC_E	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_10	SJC_EPD	https://doi.org/10.1038/nature13109	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_10	SJC_EPD	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_21	SJC_ERG	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_21	SJC_ERG	https://doi.org/10.1056/NEJMoa1403088	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_14	SJC_ETV	https://doi.org/10.1038/ng.2611	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_14	SJC_ETV	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_9	SJC_HGG	https://doi.org/10.1038/ng.2938	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_9	SJC_HGG	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_44	SJC_Other	https://doi.org/10.1056/NEJMoa1403088	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_39	SJC_HYPO	https://doi.org/10.1056/NEJMoa1403088	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_39	SJC_HYPO	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_43	SJC_INF	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_43	SJC_INF	https://doi.org/10.1038/ng.3230	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_15	SJC_LGG	https://doi.org/10.1038/ng.2611	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_15	SJC_LGG	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_38	SJC_MB	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_40	SJC_MEL	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_44	SJC_Other	https://doi.org/10.1038/ng.3230	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_29	SJC_OS	https://doi.org/10.1016/j.celrep.2014.03.003	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_29	SJC_OS	https://doi.org/10.1016/j.celrep.2014.03.003, https://doi.org/10.1158/1541-7786.mcr-18-0620	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_22	SJC_PHALL	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_44	SJC_Other	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_17	SJC_RHB	https://doi.org/10.1016/j.ccr.2013.11.002	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_17	SJC_RHB	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_45	SJC_WLM	NA	SJC	Pediatric Cancer Genome Project (PCGP)
Cohort_36	EGAD00001000826	http://dx.doi.org/10.1038/ncomms15936	EGA	EGAD00001000826
Cohort_48	SRP040454	No publication	SRA	SRP040454
Cohort_8	phs000720.v2.p1	http://dx.doi.org/10.1158/2159-8290.CD-13-0639	dbGaP	phs000720.v2.p1
Cohort_13	phs000768.v2.p1	http://dx.doi.org/10.1371/journal.pgen.1004475	dbGaP	phs000768.v2.p1
Cohort_6	phs000673.v2.p1	http://dx.doi.org/10.1001/jama.2015.10080	dbGaP	phs000673.v2.p1
Cohort_6	phs000673.v2.p1	http://dx.doi.org/10.1001/jama.2015.10080, http://dx.doi.org/10.1038/nature23306	dbGaP	phs000673.v2.p1
Cohort_6	phs000673.v2.p1	NA	dbGaP	phs000673.v2.p1
Cohort_31	EGAD00001001927	http://dx.doi.org/10.1016/j.cell.2016.01.015	EGA	EGAD00001001927
Cohort_4	EGAD00001003279	http://dx.doi.org/10.1038/nature22973	EGA	EGAD00001003279
Cohort_35	SRP092501	http://dx.doi.org/10.1126/scitranslmed.aah6904	SRA	SRP092501
Cohort_33	SRP126664	http://dx.doi.org/10.1016/j.ccell.2018.05.002	SRA	SRP126664

Genders in dataset

table(counts_and_meta$gender, useNA = "always")

## 
##       female         male not reported      unknown         <NA> 
##          739         1010          310           82           38

# samples with reported genders
counts_and_meta %>% 
  filter(! is.na(gender)) %>%
  filter(! gender %in% c("not reported", "unknown")) %>%
  tabyl(gender) %>%
  adorn_totals

##  gender    n   percent
##  female  739 0.4225272
##    male 1010 0.5774728
##   Total 1749 1.0000000

Ages in dataset

table(counts_and_meta$pedaya, useNA = "always")

## 
##                  No Yes, age < 30 years                <NA> 
##                  51                2033                  95

# at least one target sample is >30

n_ped_aya <- sum(counts_and_meta$pedaya=="Yes, age < 30 years" | counts_and_meta$age_at_dx<30, na.rm = TRUE)
n_ped_aya

## [1] 2104

n_ped_aya/nrow(counts_and_meta)

## [1] 0.9655805

# more than 95% of which are pediatric <30; of the Datasets with specific ages at diagnosis reported, the median was 7

median(counts_and_meta$age_at_dx, na.rm = TRUE)

## [1] 8.96

table(is.na(counts_and_meta$age_at_dx))

## 
## FALSE  TRUE 
##  1739   440

Read lengths in dataset

seq_len_round_25 <- round(counts_and_meta$seq_length / 25) * 25
table(seq_len_round_25, useNA = "always")

## seq_len_round_25
##   25   50   75  100  125 <NA> 
##    4  238  366 1544   27    0

tabyl(seq_len_round_25)

##  seq_len_round_25    n     percent
##                25    4 0.001835704
##                50  238 0.109224415
##                75  366 0.167966957
##               100 1544 0.708581918
##               125   27 0.012391005

table(is.na(counts_and_meta$seq_length))

## 
## FALSE 
##  2179

table(counts_and_meta$seq_length)

## 
##   36   50   51   75   76   81  100  101  110  125 
##    4   11  227  279   40   47   98 1431   15   27

summary(counts_and_meta$seq_length)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   36.00   76.00  101.00   91.51  101.00  125.00

IQR(counts_and_meta$seq_length, na.rm = TRUE)

## [1] 25

 old_read_length_histogram <-  ggplot(counts_and_meta) + 
  geom_histogram(aes(seq_length, group=cohort_code), 
                 fill = "lightgrey", color = "black", stat = StatBin2) +
      theme_minimal(base_size = 20) +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +
  xlab("Sequence length") +
    ylab("Datasets")
 
 read_length_histogram <-  ggplot(counts_and_meta) + 
  geom_histogram(aes(seq_length), 
                 fill = "lightgrey", color = "black", stat = StatBin2,
                 binwidth = 1) +
      theme_minimal(base_size = 20) +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +
  xlab("Sequence length") +
    ylab("Datasets") +
   expand_limits(x=0)
  
read_length_histogram

subset(counts_and_meta, Mapped > Total_reads)

## # A tibble: 0 x 23
## # Groups:   cohort_code [0]
## # … with 23 variables: dataset_id <chr>, Total_reads <dbl>, Mapped <dbl>,
## #   MND <dbl>, MEND <dbl>, seq_length <dbl>, disease <chr>,
## #   age_at_dx <dbl>, pedaya <chr>, gender <chr>, cohort_code <chr>,
## #   cohort_name <chr>, doi_link <chr>, source_repository <chr>,
## #   repository_cohort_accession <chr>, repository_dataset_accession <chr>,
## #   genes_expressed_above_0 <dbl>, genes_expressed_above_1 <dbl>,
## #   genes_expressed_above_2 <dbl>, genes_expressed_above_3 <dbl>,
## #   genes_expressed_above_4 <dbl>, genes_expressed_above_5 <dbl>,
## #   n_in_cohort <int>

subset(counts_and_meta, MND > Mapped)

## # A tibble: 0 x 23
## # Groups:   cohort_code [0]
## # … with 23 variables: dataset_id <chr>, Total_reads <dbl>, Mapped <dbl>,
## #   MND <dbl>, MEND <dbl>, seq_length <dbl>, disease <chr>,
## #   age_at_dx <dbl>, pedaya <chr>, gender <chr>, cohort_code <chr>,
## #   cohort_name <chr>, doi_link <chr>, source_repository <chr>,
## #   repository_cohort_accession <chr>, repository_dataset_accession <chr>,
## #   genes_expressed_above_0 <dbl>, genes_expressed_above_1 <dbl>,
## #   genes_expressed_above_2 <dbl>, genes_expressed_above_3 <dbl>,
## #   genes_expressed_above_4 <dbl>, genes_expressed_above_5 <dbl>,
## #   n_in_cohort <int>

subset(counts_and_meta, MEND > MND)

## # A tibble: 0 x 23
## # Groups:   cohort_code [0]
## # … with 23 variables: dataset_id <chr>, Total_reads <dbl>, Mapped <dbl>,
## #   MND <dbl>, MEND <dbl>, seq_length <dbl>, disease <chr>,
## #   age_at_dx <dbl>, pedaya <chr>, gender <chr>, cohort_code <chr>,
## #   cohort_name <chr>, doi_link <chr>, source_repository <chr>,
## #   repository_cohort_accession <chr>, repository_dataset_accession <chr>,
## #   genes_expressed_above_0 <dbl>, genes_expressed_above_1 <dbl>,
## #   genes_expressed_above_2 <dbl>, genes_expressed_above_3 <dbl>,
## #   genes_expressed_above_4 <dbl>, genes_expressed_above_5 <dbl>,
## #   n_in_cohort <int>

Summarize total read counts

min(counts_and_meta$Total_reads)

## [1] 206916

Total_read_counts <- counts_and_meta %>% 
  ungroup %>%
  mutate(dataset_value = Total_reads/1e6) %>%
  summarize(variance= var(dataset_value),
            min = min(dataset_value),
            max = max(dataset_value),
            median = median(dataset_value),
            p25 = quantile(dataset_value, 0.25),
            p75 = quantile(dataset_value, 0.75),
            iqr = IQR(dataset_value))

kable(Total_read_counts %>%
        select(-variance, -p25, -p75), digits = 1)

min	max	median	iqr
0.2	668	61.2	53.3

total_read_count_histogram <-  ggplot(counts_and_meta) + 
  geom_histogram(aes(Total_reads/1E6), 
                 fill = "lightgrey", color = "black", stat = StatBin2) +
      theme_minimal(base_size = 20) +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +
  xlab("Total reads") +
    ylab("Datasets")

total_read_count_histogram

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Calculate percentages

 read_counts_with_read_type_fractions <- counts_and_meta  %>%
  arrange(desc(Total_reads)) %>%
  mutate(
    pct_unmapped_of_total = (Total_reads - Mapped) / Total_reads,
    pct_dupe_of_mapped = (Mapped - MND) / Mapped,
    pct_non_exonic_of_non_dupe = (MND - MEND) / MND
  )

read_type_fractions_long <- read_counts_with_read_type_fractions %>%
  pivot_longer(cols = starts_with("pct_"), names_to = "read_type_fraction_name", values_to = "dataset_value")

Summarize read type fractions

comparison_of_distributions <- read_type_fractions_long %>% 
  group_by(read_type_fraction_name) %>%
  summarize(variance= var(dataset_value),
            min = min(dataset_value),
            max = max(dataset_value),
            median = median(dataset_value),
            p25 = quantile(dataset_value, 0.25),
            p75 = quantile(dataset_value, 0.75),
            iqr = IQR(dataset_value))

kable(comparison_of_distributions %>%
      mutate_if(is.double, ~100*.), digits = 3)

read_type_fraction_name	variance	min	max	median	p25	p75	iqr
pct_dupe_of_mapped	5.884	2.853	99.997	26.887	13.450	43.033	29.583
pct_non_exonic_of_non_dupe	4.094	4.495	97.000	24.769	16.189	37.277	21.088
pct_unmapped_of_total	0.605	0.710	76.677	3.414	2.740	6.006	3.266

kable(comparison_of_distributions %>%
        select(-variance, -p25, -p75) %>%
      mutate_if(is.double, ~100*.), digits = 0)

read_type_fraction_name	min	max	median	iqr
pct_dupe_of_mapped	3	100	27	30
pct_non_exonic_of_non_dupe	4	97	25	21
pct_unmapped_of_total	1	77	3	3

statements about read type fractions

Overview

comparison_of_distributions

## # A tibble: 3 x 8
##   read_type_fraction_na… variance     min   max median    p25    p75    iqr
##   <chr>                     <dbl>   <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
## 1 pct_dupe_of_mapped      0.0588  0.0285  1.00  0.269  0.135  0.430  0.296 
## 2 pct_non_exonic_of_non…  0.0409  0.0450  0.970 0.248  0.162  0.373  0.211 
## 3 pct_unmapped_of_total   0.00605 0.00710 0.767 0.0341 0.0274 0.0601 0.0327

unmapped

# in 77 datasets, more than 25% of reads are unmapped
table(read_counts_with_read_type_fractions$pct_unmapped_of_total>0.25)

## 
## FALSE  TRUE 
##  2102    77

duplicate

# 426 datasets have more than 50% duplicates (Fig 2A).
read_counts_with_read_type_fractions %>%
  mutate(above_50 = pct_dupe_of_mapped>0.50) %>%
  tabyl(above_50)

##  above_50    n   percent
##     FALSE 1753 0.8044975
##      TRUE  426 0.1955025

fiftypct <- read_counts_with_read_type_fractions %>%
  group_by(cohort_code) %>%
  summarize(has_a_dataset_with_more_than_50pct_dupes = any(pct_dupe_of_mapped>0.50),
            median_pct_dupe_of_mapped = median(pct_dupe_of_mapped)) %>%
  mutate(median_lt_50 = median_pct_dupe_of_mapped < 0.5,
         median_lt_50_and_has_a_dataset = median_lt_50 & has_a_dataset_with_more_than_50pct_dupes)


fiftypct %>% tabyl(median_lt_50)

##  median_lt_50  n   percent
##         FALSE  7 0.1458333
##          TRUE 41 0.8541667

fiftypct %>% tabyl(has_a_dataset_with_more_than_50pct_dupes)

##  has_a_dataset_with_more_than_50pct_dupes  n percent
##                                     FALSE 15  0.3125
##                                      TRUE 33  0.6875

fiftypct %>% tabyl(median_lt_50_and_has_a_dataset)

##  median_lt_50_and_has_a_dataset  n   percent
##                           FALSE 22 0.4583333
##                            TRUE 26 0.5416667

exonic

read_counts_with_read_type_fractions %>%
  mutate(above_50 = pct_non_exonic_of_non_dupe>0.50) %>%
  tabyl(above_50)

##  above_50    n   percent
##     FALSE 1849 0.8485544
##      TRUE  330 0.1514456

fiftypcte <- read_counts_with_read_type_fractions %>%
  group_by(cohort_code) %>%
  summarize(has_a_dataset_with_more_than_50pct_ne = any(pct_non_exonic_of_non_dupe>0.50),
            median_pct_dupe_of_mapped = median(pct_non_exonic_of_non_dupe)) %>%
  mutate(median_lt_50 = median_pct_dupe_of_mapped < 0.5,
         median_lt_50_and_has_a_dataset = median_lt_50 & has_a_dataset_with_more_than_50pct_ne)


fiftypcte %>% tabyl(median_lt_50)

##  median_lt_50  n percent
##         FALSE  3  0.0625
##          TRUE 45  0.9375

fiftypcte %>% tabyl(has_a_dataset_with_more_than_50pct_ne)

##  has_a_dataset_with_more_than_50pct_ne  n percent
##                                  FALSE 36    0.75
##                                   TRUE 12    0.25

fiftypcte %>% tabyl(median_lt_50_and_has_a_dataset)

##  median_lt_50_and_has_a_dataset  n percent
##                           FALSE 39  0.8125
##                            TRUE  9  0.1875

Correlation of depth of sequencing to duplicate read fraction

Does the depth of sequencing correlate to the number of duplicates? How much variance does that explain?

cor_tot_reads_and_dupes <- cor(read_counts_with_read_type_fractions$Total_reads, 
    read_counts_with_read_type_fractions$pct_dupe_of_mapped,
    method = c("pearson"))
round(cor_tot_reads_and_dupes, 2)

## [1] 0.58

round(cor_tot_reads_and_dupes^2, 2)

## [1] 0.34

Plot correlation of depth of sequencing to duplicate read fraction

this_data <- read_counts_with_read_type_fractions %>%
  select(Total_reads, pct_dupe_of_mapped)

## Adding missing grouping variables: `cohort_code`

this_plot_title <- paste("Correlation of Total_reads and % Duplicates")

these_stats <- this_data  %>%
  ungroup %>%
  summarize(r_corr = round(cor(Total_reads, pct_dupe_of_mapped), 2))

ggplot(this_data,
       aes(x=Total_reads/1E6, y=pct_dupe_of_mapped)) + 
  geom_point(alpha = 0.5, shape = 20, size =0.1) +
  geom_smooth(method = "lm", color = "black", se = FALSE) +
  geom_label(data = these_stats, 
             aes(label=paste0("r=", r_corr)), 
             x=max(this_data$Total_reads/1E6),
             y=max(this_data$pct_dupe_of_mapped),
             hjust = 1, vjust = 1
             ) +
    theme_minimal(base_size = 20) +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +
  ggtitle(this_plot_title)  + 
  theme(legend.position="none", plot.title=element_text(size=22)) +
  xlab("Read counts (million)") +
  ylab("% Duplicates")

## `geom_smooth()` using formula 'y ~ x'

Generate schematic defining read types

colors_for_read_types <- these_colors_with_MEND
names(colors_for_read_types) <- gsub(" reads", "", names(these_colors_with_MEND))

colors_for_read_types <- c(colors_for_read_types, 
                           "Genome" = "darkblue",
                           "Exon" = "darkblue",
                           "Aligned reads" = "darkgrey")

plot1_colnames <- c("label", "x1", "x2", "y1", "y2")

read_type_schematic_data_as_vector <- c("Duplicate", 3, 4, 5, 6, 
                       "MEND", 3, 4, 4, 5, 
                       "MEND", 2.5, 3.5, 3, 4, 
                       "Non-exonic", 4.5, 5.5, 3, 4,
                       "Unmapped", 6, 7, 3, 4,
                       "Exon", 2.5, 4, 1, 2,
                       "Aligned reads", 2.8, 2.8, 6, 7)

rows_in_schematic <- length(read_type_schematic_data_as_vector)/length(plot1_colnames)

read_type_schematic_data <- matrix(read_type_schematic_data_as_vector, 
                     ncol = length(plot1_colnames), byrow = TRUE,
                     dimnames = list(c(1:rows_in_schematic), plot1_colnames)) %>%
  as_tibble %>%
  type_convert() %>%
  mutate(
    base_color = colors_for_read_types[match(label, names(colors_for_read_types))],
    border_color = case_when(
      label %in% c("Aligned reads", "Genome", "MEND", "Exon") ~ NA_character_,
      TRUE ~ base_color),
    fill_color = ifelse(label %in% c("MEND", "Exon"),  base_color, "white"),
    text_color = case_when(
      label %in% c("MEND", "Exon")~  "white",
      TRUE ~ base_color),
    mid_bar_x = map2_dbl(x1, x2,  function(x, y) mean(c(x,y))),
    mid_bar_y = map2_dbl(y1, y2,  function(x, y) mean(c(x,y)))
  )

## Parsed with column specification:
## cols(
##   label = col_character(),
##   x1 = col_double(),
##   x2 = col_double(),
##   y1 = col_double(),
##   y2 = col_double()
## )

padding_size = 0.35
end_of_box <- sort(read_type_schematic_data$x2,partial=length(read_type_schematic_data$x2)-1)[length(read_type_schematic_data$x2)-1] + padding_size
read_type_schematic <- ggplot(read_type_schematic_data, 
                              aes(xmin=x1, xmax=x2, ymin=y1, ymax = y2, 
                                  fill = fill_color, color = border_color)) +
  geom_rect() +
  
  geom_segment(x=min(read_type_schematic_data$x1-padding_size), y=1.5, xend = 5.75, yend = 1.5, color = "darkblue") +   
  geom_text(aes(label = label, x = mid_bar_x, y=mid_bar_y,
                color = text_color), 
            size = 4,
            fontface = "bold") +
  geom_rect(aes(xmin = min(x1 - padding_size), 
                ymin = 2.5, 
                xmax = end_of_box,
                ymax=max(y2 + padding_size)), 
            fill = NA, color = "darkgrey", size = 0.25) +
  scale_fill_identity() +
  scale_color_identity() +
  theme_void() + 
  theme(
    panel.grid.major.x = element_line(color="lightgrey", size=0.2),
    ) + 
 scale_x_continuous(
   breaks = seq(min(read_type_schematic_data$x1) - padding_size + 0.25, end_of_box , 0.25))

read_type_schematic

Generate schematic defining read type fractions

stat_names <- c("pct_unmapped_of_total",  "pct_dupe_of_mapped", "pct_non_exonic_of_non_dupe")
stat_labels <- c("Unmapped",  "Duplicate", "Non-exonic") 
complement_stat_names <- c("Mapped reads",  "Non-duplicate reads", "Exonic reads") 
divisor_name = c("total", "mapped", "non-duplicate")

comparison_of_remaining <- read_type_fractions_long %>% 
  mutate(read_type_fraction_name = complement_stat_names[match(read_type_fraction_name, stat_names)]) %>%
  group_by(read_type_fraction_name) %>%
  summarize(min = round(1-max(dataset_value), 2),
    max = round(1-min(dataset_value), 2),
    median = round(1-median(dataset_value), 2),
    p25 = round(1-quantile(dataset_value, 0.25), 2),
    p75 = round(1-quantile(dataset_value, 0.75), 2)) %>%
      mutate(bar_label = paste0 (read_type_fraction_name, "\n(", 100*min, "-", 100*max, "% of ", divisor_name[match(read_type_fraction_name, complement_stat_names)], "; x͂=",100* median, "%)"))

positive_bars <- comparison_of_remaining %>%
  select(-min, -max, -p25, -p75) %>%
  mutate(read_type_fraction_label = read_type_fraction_name, 
         position = "1") %>%
  rename(abs_median = median)

negative_bars <- tibble(read_type_fraction_name = positive_bars$read_type_fraction_name,
                        read_type_fraction_label = stat_labels[match(read_type_fraction_name, complement_stat_names)],
                        bar_label = read_type_fraction_label,
                        abs_median = 1-positive_bars$abs_median,
                        position = "2")

total_bar <- tibble(read_type_fraction_name = "Total reads",
                    read_type_fraction_label = read_type_fraction_name,
                    bar_label = "Total reads\n(100% of reads)",
                    abs_median = 1,
                    position = "1"
                    )


MEND_stats_of_total <- counts_and_meta  %>%
  arrange(desc(Total_reads)) %>%
  mutate(
    pct_MEND_of_total = (Total_reads - MEND) / Total_reads
  ) %>%
  ungroup %>% 
  summarize(min = round(1-max(pct_MEND_of_total), 2),
    max = round(1-min(pct_MEND_of_total), 2),
    median = round(1-median(pct_MEND_of_total), 2))

MEND_bar <- tibble(read_type_fraction_name = "MEND reads",
                    read_type_fraction_label = read_type_fraction_name,
                    bar_label = with(MEND_stats_of_total, paste0 ("MEND reads\n(", 100*min, "-", 100*max, "% of total reads; x͂=",100* median, "%)")),
                    abs_median = 1,
                    position = "1"
                    )

bar_names_in_order <- c("Total reads", "Mapped reads", "Non-duplicate reads", "Exonic reads", "MEND reads")
  
all_bars <- bind_rows(positive_bars, negative_bars, total_bar, MEND_bar) %>%
  mutate(read_type_fraction_name = factor(read_type_fraction_name, 
                                          levels = bar_names_in_order))

cat_space <- all_bars %>% filter(position == 1) %>%
  arrange(read_type_fraction_name) %>%
  select(read_type_fraction_name, abs_median) %>%
  ungroup %>%
  mutate(lagged1_abs_median = lag(abs_median, default =1),
         lagged2_abs_median = lag(abs_median, n=2, default =1),
         category_space = lagged1_abs_median*lagged2_abs_median
         ) %>%
  select(read_type_fraction_name, category_space)

these_colors_with_MEND = c(these_colors, "MEND reads"="#000000")


all_bars_rel <- left_join(all_bars, cat_space, by = "read_type_fraction_name") %>%
  mutate(rel_median=ifelse(read_type_fraction_name == "MEND reads",
                           abs_median[read_type_fraction_label=="Exonic reads"]*
                             category_space[read_type_fraction_label=="Exonic reads"], 
                           abs_median*category_space),
         read_type_fraction_name = factor(read_type_fraction_name, levels = rev(bar_names_in_order)),
         position = factor(position, levels = rev(sort(unique(position)))),
         bar_color = these_colors_with_MEND[match(read_type_fraction_name, names(these_colors_with_MEND))],
         fill_val = ifelse(position == 1, bar_color, NA),
         border_color_val = bar_color,
         text_color = ifelse(position ==1, "white", "black"),
font_size = ifelse(abs_median<0.1, 3,3.5),
text_angle = ifelse(abs_median<0.1, 90,0)
         )

read_type_fractions_schematic <- ggplot(all_bars_rel, aes(y=read_type_fraction_name,
                x=rel_median, group = position)) + 
   geom_col(aes(fill = fill_val,
                color = border_color_val
                )) +
   geom_text(aes(label=bar_label,
                 color = text_color,
                 size = font_size,
                 angle= text_angle), 
             position = position_stack(vjust = .5),
              fontface = "bold") +
   scale_fill_identity() +
   scale_color_identity()  +
  scale_size_identity() + 
   theme_void() +
  theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank()) 


ggplot(all_bars_rel, aes(y=read_type_fraction_name,
                x=rel_median, group = position)) + 
   geom_col(aes(fill = fill_val,
                color = border_color_val
                )) +
   geom_text(aes(label=read_type_fraction_label,
                 color = text_color,
                 size = font_size,
                 angle = text_angle), 
             position = position_stack(vjust = .5),
              fontface = "bold") +
   scale_fill_identity() +
   scale_color_identity()  +
  scale_size_identity() + 
   theme_void() +
  theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())

read_type_fractions_schematic

figure_name <- "f1"

right_side <- plot_grid(read_type_schematic,
read_type_fractions_schematic,
nrow=2,
rel_heights = c(1, 2),
labels = c("C", "D"))

left_side <- plot_grid(cohort_size_histogram,
read_length_histogram,
nrow=2,
labels = c("A", "B"))

plot_grid(left_side,
right_side,
ncol = 2) +
   draw_label(paste(figure_name, Sys.time()),
              x = 0, y = 0.02, hjust = 0, size = 6)

stat_names <- c("pct_unmapped_of_total",  "pct_dupe_of_mapped", "pct_non_exonic_of_non_dupe")
stat_label <- c("% unmapped", "% duplicate \n(of mapped)", "% non-exonic \n(of non-duplicate)")
names(stat_label) <- stat_names

Plot read fractions

colors_for_stat_names <- these_colors[c("Mapped reads", "Non-duplicate reads","Exonic reads")]
names(colors_for_stat_names) <- stat_names

these_colors <- c("#A6CEE3", "#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99", "#E31A1C", 
"#FDBF6F", "#FF7F00")
# light blue, dark blue, etc: green, red, orange (then purple and brown)
names(these_colors) <-  c("not assigned", "Total reads", "Non-exonic reads",  "Exonic reads", "Duplicate reads",  "Non-duplicate reads",  "Unmapped reads", "Mapped reads")

# 6 x 11 was a good ratio, but the text was too small

read_type_fractions_long_for_histogram <- read_type_fractions_long %>%
  mutate(read_type_fraction_name = factor(read_type_fraction_name,
                                          levels = stat_names))

read_type_fractions_histogram <-  ggplot(read_type_fractions_long_for_histogram) +
    geom_histogram(aes(x = dataset_value, color = read_type_fraction_name), 
                   fill = "white", stat = StatBin2)  +
  scale_color_manual(values = colors_for_stat_names) +
  facet_wrap(~read_type_fraction_name, 
             nrow = 1, 
             scales = "free_y",
             labeller = labeller(
               read_type_fraction_name = stat_label
               )
             ) +
  ylab("Datasets") +
  xlab(paste0("Read type percentages in ", length(unique(read_type_fractions_long_for_histogram$dataset_id)),  " datasets")) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.5)) +
    theme_minimal() +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +
    theme(strip.text.x = element_text(size = 12, face = "bold")) +
    theme(legend.position = "none")

read_type_fractions_histogram

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Calculate MEND reads as a fraction of total

read_counts_with_MEND_fractions <- counts_and_meta  %>%
  arrange(desc(Total_reads)) %>%
  mutate(pct_MEND_of_total = MEND /Total_reads)

ggplot(read_counts_with_MEND_fractions) + 
  geom_histogram(aes(pct_MEND_of_total), stat = StatBin2) +
    theme_minimal(base_size = 20) +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

MEND_pct <- read_counts_with_MEND_fractions %>% 
  ungroup %>%
  mutate(dataset_value = pct_MEND_of_total) %>%
  summarize(variance= var(dataset_value),
            min = min(dataset_value),
            max = max(dataset_value),
            median = median(dataset_value),
            p25 = quantile(dataset_value, 0.25),
            p75 = quantile(dataset_value, 0.75),
            iqr = IQR(dataset_value))

kable(MEND_pct %>%
        select(-variance, -p25, -p75) %>%
      mutate_if(is.double, ~100*.), digits = 0)

min	max	median	iqr
0	79	50	31

head(sort(read_counts_with_MEND_fractions$pct_MEND_of_total))

## [1] 0.0000267009 0.0002533711 0.0003651723 0.0003767257 0.0003997294
## [6] 0.0004279584

sum(read_counts_with_MEND_fractions$pct_MEND_of_total<0.25)

## [1] 355

read_counts_with_MEND_fractions %>%
  ungroup %>%
  mutate(pct_under_25 = pct_MEND_of_total<0.25) %>%
  tabyl(pct_under_25) %>%
  adorn_totals()

##  pct_under_25    n   percent
##         FALSE 1824 0.8370812
##          TRUE  355 0.1629188
##         Total 2179 1.0000000

read_counts_with_MEND_fractions %>%
  ungroup %>%
  filter(Mapped > 30E6) %>%
  mutate(pct_under_25 = pct_MEND_of_total<0.25) %>%
  tabyl(pct_under_25) %>%
  adorn_totals()

##  pct_under_25    n   percent
##         FALSE 1739 0.8368624
##          TRUE  339 0.1631376
##         Total 2078 1.0000000

Show per-cohort distribution of read_type_fractions

figure_name <- "fracs_by_group"

read_type_fractions_long_for_boxplot <- read_type_fractions_long_for_histogram %>%
  ungroup %>%
  mutate(boxplot_label = fct_reorder(paste0(as.character(cohort_code), ", n=", n_in_cohort), n_in_cohort))


read_type_fractions_per_cohort_boxplot <- ggplot(read_type_fractions_long_for_boxplot) +
  geom_boxplot(aes(x = dataset_value, y=boxplot_label), outlier.size = 0.5)  +
  facet_wrap(~read_type_fraction_name, 
             nrow = 1, 
             labeller = labeller(
               read_type_fraction_name = stat_label
             )
  ) +
  ylab("Cohorts") +
  xlab(paste0("Read type percentages in ", length(unique(read_type_fractions_long_for_boxplot$dataset_id)),  " datasets")) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.5)) +
    theme_minimal() +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +
  theme(strip.text.x = element_text(size = 12, face = "bold")) +
  theme(legend.position = "none")


read_type_fractions_per_cohort_boxplot

Characterize Cohort 4

read_type_names <- c("Total_reads", "Mapped",  "MND", "MEND")

this_cohort_name <- "EGAD00001003279"

this_cohort_code <- subset(read_counts_with_read_type_fractions, cohort_name == this_cohort_name)$cohort_code[1]

this_cohort_code

## [1] "Cohort_4"

in MS

 read_counts_with_read_type_fractions %>%
  filter(cohort_code == this_cohort_code) %>%
   mutate(gt_98 = pct_dupe_of_mapped > 0.98) %>%
   tabyl(gt_98) %>%
   add_totals_row

## Warning: 'add_totals_row' is deprecated.
## Use 'adorn_totals("row")' instead.
## See help("Deprecated")

##  gt_98   n   percent
##  FALSE  55 0.4330709
##   TRUE  72 0.5669291
##  Total 127 1.0000000

read_counts_with_read_type_fractions %>%
  filter(cohort_code == this_cohort_code) %>%
  filter(pct_dupe_of_mapped > 0.98) %>%
  pull(Total_reads) %>% min

## [1] 178294341

other

 counts_and_meta_long <- counts_and_meta  %>%
  pivot_longer(cols = c(Total_reads, Mapped, MEND, MND), names_to = "read_type_name", values_to = "dataset_value")  %>%
   ungroup %>%
   mutate(read_type_name = factor(read_type_name, levels = read_type_names))

counts_and_meta_long_one_proj <-  counts_and_meta_long %>%
  filter(cohort_code == this_cohort_code)

table(counts_and_meta_long_one_proj$read_type_name)

## 
## Total_reads      Mapped         MND        MEND 
##         127         127         127         127

# 78 datasets in the cohort have fewer than 0.2 million MEND reads. 
sum((counts_and_meta_long_one_proj %>% filter(read_type_name=="MEND"))$dataset_value < 200000)

## [1] 78

those_datasets <- counts_and_meta_long_one_proj %>% filter(read_type_name=="MEND", dataset_value < 200000) %>% pull(dataset_id)


summary((counts_and_meta_long_one_proj %>% filter(read_type_name=="Total_reads", dataset_id %in% those_datasets))$dataset_value)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 178294341 199916664 208667776 211606891 223832191 247618832

max_plots_to_include <- 10
n_datasets_to_plot <- ifelse(n_distinct(counts_and_meta_long_one_proj$dataset_id)>max_plots_to_include, 
                         max_plots_to_include, 
                         n_distinct(counts_and_meta_long_one_proj$dataset_id))
plot_word <- ifelse(n_datasets_to_plot==max_plots_to_include, max_plots_to_include, paste("all", n_distinct(counts_and_meta_long_one_proj$dataset_id)))

set.seed(100)
datasets_to_plot <- sample(unique(counts_and_meta_long_one_proj$dataset_id), n_datasets_to_plot)

ggplot(subset(counts_and_meta_long_one_proj, dataset_id %in% datasets_to_plot)) + 
  geom_col(aes(x=read_type_name, y=dataset_value/1e6, fill = read_type_name)) +
  facet_wrap(~dataset_id, ncol = 1, strip.position="right")  +
    theme_minimal(base_size = 20) +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +
  theme(strip.text.y = element_text(angle = 0)) +
  scale_fill_brewer(palette = "Set1")  +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  ggtitle(paste("Read counts for", plot_word, "datasets from", this_cohort_code)) +
  coord_flip()

ggplot(counts_and_meta_long_one_proj %>% filter(read_type_name == "MEND")) + 
  geom_histogram(aes(dataset_value/1e6), stat = StatBin2) +
    theme_minimal(base_size = 20) +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +
  ggtitle(paste("Distribution of MEND counts in", this_cohort_code))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

bl <- colorRampPalette(c("navy","royalblue","lightskyblue"))(200)                      
re <- colorRampPalette(c("mistyrose", "red2","darkred"))(200)


ggplot(counts_and_meta %>% 
         filter(cohort_code == this_cohort_code) %>%
         mutate(pct_dupes = (Mapped - MND)/Mapped)) + 
  geom_point(aes(x=MEND/1e6, y=pct_dupes, fill = Total_reads/1e6), shape=21, color = "black") +
    theme_minimal(base_size = 20) +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +
  scale_fill_gradientn(colours = c(bl,"white", re)) +
  ggtitle(paste("Relationship of dupe fraction to MEND count in", this_cohort_code), "For detecting whether datasets with less info were sequenced more deeply. \nNormally, higher depth datasets have more MEND reads and more duplicate \nreads than the same dataset sequenced at a lower total depth.")

counts_and_meta_long_one_proj %>%
  select(seq_length, dataset_id) %>%
  distinct %>%
  tabyl(seq_length)

##  seq_length   n percent
##          51 127       1

Expressed genes

What is the distribution of measured genes?

gene_counts_long <- counts_and_meta %>%
  pivot_longer(cols = starts_with("genes_"), names_to = "Expression_threshold")  %>%
  mutate(Expression_threshold = paste(str_replace(Expression_threshold, "genes_expressed_above_", "genes > "), "TPM"))  


ggplot(gene_counts_long) + 
  geom_histogram(aes(x=value/1e3, fill = Expression_threshold), stat = StatBin) +
  facet_wrap(~Expression_threshold)  +
    theme_minimal() +
  scale_fill_brewer(palette = "Set1") +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +
#  theme_linedraw(base_size = 20) +
    theme(legend.position = "none") +
  # scale_fill_brewer(palette = "Set1") +
  ylab("Datasets") +
  xlab("Measured genes")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

What are the exact lowest gene counts?

sort(counts_and_meta$genes_expressed_above_0) [1:100]

##   [1]     9     9    12    12    13    14    14    15    16    16    17
##  [12]    18    18    18    18    18    18    19    19    19    19    20
##  [23]    20    20    20    20    20    21    21    21    21    21    21
##  [34]    22    22    22    23    23    23    23    23    24    24    24
##  [45]    24    24    24    24    25    25    25    26    26    26    26
##  [56]    27    27    27    27    28    28    28    28    29    29    29
##  [67]    29    29    30    30    31    31    32    33    35    36    37
##  [78]    40  6098  7107 11830 15323 16793 16837 16943 17233 17531 18288
##  [89] 18588 18704 19626 19693 19749 19781 19942 19963 20063 20120 20203
## [100] 20247

correlations with all datasets

library(corrr)


counts_and_meta %>% 
    ungroup %>% 
    select_if(is.double) %>%
       correlate() %>%
  filter(rowname %in% read_type_names) %>%
  rename(Read_type_count=rowname) %>% 
  mutate(Read_type_count = factor(Read_type_count, levels = read_type_names)) %>%
  arrange(Read_type_count) %>%
  select(c(Read_type_count, starts_with("genes_expressed"))) %>%
  kable(caption = "Correlations with all datasets", digits = 2)

## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

Correlations with all datasets
Read_type_count	genes_expressed_above_0	genes_expressed_above_1	genes_expressed_above_2	genes_expressed_above_3	genes_expressed_above_4	genes_expressed_above_5
Total_reads	-0.20	-0.40	-0.40	-0.40	-0.39	-0.39
Mapped	-0.19	-0.41	-0.41	-0.41	-0.40	-0.39
MND	0.51	0.24	0.20	0.18	0.17	0.16
MEND	0.48	0.27	0.26	0.26	0.26	0.26

correlations with all but low gene count datasets

counts_and_meta %>% 
  filter(genes_expressed_above_0 > min_genes_gt_0) %>%
    ungroup %>% 
    select_if(is.double) %>%
       correlate() %>%
  filter(rowname %in% read_type_names) %>%
  rename(Read_type_count=rowname) %>% 
  mutate(Read_type_count = factor(Read_type_count, levels = read_type_names)) %>%
  arrange(Read_type_count) %>%
  select(c(Read_type_count, starts_with("genes_expressed"))) %>%
  kable(caption = "Correlations with all but low gene count datasets", digits = 2)

## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

Correlations with all but low gene count datasets
Read_type_count	genes_expressed_above_0	genes_expressed_above_1	genes_expressed_above_2	genes_expressed_above_3	genes_expressed_above_4	genes_expressed_above_5
Total_reads	0.13	-0.26	-0.28	-0.28	-0.28	-0.28
Mapped	0.21	-0.25	-0.26	-0.27	-0.27	-0.27
MND	0.51	0.04	0.02	0.00	0.00	0.00
MEND	0.44	0.08	0.09	0.11	0.11	0.12

Plot correlations betweeen gene counts and read types

Groom data

expr_gene_and_read_counts_to_plot <- counts_and_meta %>%
  pivot_longer (cols = c(MND, MEND, Total_reads, Mapped), names_to = "Read_type", values_to = "Read_count") %>%
  pivot_longer (cols = starts_with("genes_expressed"), names_to = "Expression_threshold", values_to = "Gene_count") %>%
  mutate(Read_type = factor(Read_type, levels = read_type_names))

Identify datasets with unusual gene counts or read depths

datasets_with_normal_expressed_gene_numbers <- counts_and_meta %>%
  filter(genes_expressed_above_0 > min_genes_gt_0) %>%
  pull(dataset_id)

datasets_with_outlier_gene_counts <- counts_and_meta %>%
  ungroup %>%
  filter(is_outlier(genes_expressed_above_0)) %>%
  pull(dataset_id) 

datasets_with_outlier_depths <- counts_and_meta %>%
  ungroup %>%
  filter(is_outlier(Total_reads)) %>%
  pull(dataset_id)

Function for calculating gene count correlations and plotting results

plot_gene_count_correlations <- function(this_data = expr_gene_and_read_counts_to_plot, this_plot_title = "gene count correlations"){

this_data <- this_data %>% 
  mutate(Expression_threshold = paste(str_replace(Expression_threshold, "genes_expressed_above_", "genes > "), "TPM"))  
  
  
these_stats <- this_data  %>%
group_by(Read_type, Expression_threshold) %>%
  summarize(r_corr = round(cor(Gene_count, Read_count), 2)) 

# unique(this_data$Read_type)
# dput(unique(this_data$Read_type))
# these_colors_with_MEND

colors_for_correlation_plot <- c("Total_reads"="#1F78B4", "Mapped"="#FF7F00", 
"MND"="#E31A1C", "MEND"="#000000")

ggplot(this_data,
       aes(x=Read_count/1E6, y=Gene_count/1e3, color = Read_type)) + 
  geom_point(alpha = 0.5, shape = 20, size =0.1) +
  geom_smooth(method = "lm", color = "black") +
  geom_label(data = these_stats, 
             aes(label=paste0("r=", r_corr)), 
             x=max(this_data$Read_count/1E6),
             y=max(this_data$Gene_count/1e3),
             hjust = 1, vjust = 1
             ) +
    theme_minimal() +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5)) +
  scale_color_manual(values = colors_for_correlation_plot) +
  facet_grid(Read_type~Expression_threshold) +
  ggtitle(this_plot_title)  + 
  theme(legend.position="none") +
  xlab("Read counts (million)") +
  ylab("Gene counts (thousand)")

}

for all datasets

plot_gene_count_correlations(expr_gene_and_read_counts_to_plot,
                             "Correlations calculated across all datasets")

## `geom_smooth()` using formula 'y ~ x'

plot_gene_count_correlations(expr_gene_and_read_counts_to_plot %>% 
  filter(dataset_id %in% datasets_with_normal_expressed_gene_numbers),
  paste("Correlations in datasets with more than", min_genes_gt_0, "genes expressed above 0"))

## `geom_smooth()` using formula 'y ~ x'

plot_gene_count_correlations(expr_gene_and_read_counts_to_plot %>% 
  filter(! dataset_id %in% datasets_with_outlier_gene_counts),
  paste("Correlations calculated across all but outlier gene count datasets"))

## `geom_smooth()` using formula 'y ~ x'

plot_gene_count_correlations(expr_gene_and_read_counts_to_plot %>% 
  filter(! dataset_id %in% datasets_with_outlier_depths),
  paste("Correlations calculated across all but outlier read depth datasets"))

## `geom_smooth()` using formula 'y ~ x'

plot_gene_count_correlations(expr_gene_and_read_counts_to_plot %>% 
  filter(! dataset_id %in% datasets_with_outlier_depths, 
         ! dataset_id %in% datasets_with_outlier_gene_counts
         ),
  paste("Correlations calculated across all but datasets with outlier read depth or gene count"))

## `geom_smooth()` using formula 'y ~ x'

plot_gene_count_correlations(
  expr_gene_and_read_counts_to_plot %>% 
    filter(disease ==  "acute lymphoblastic leukemia"),
  "Correlations calculated among acute lymphoblastic leukemia datasets")

## `geom_smooth()` using formula 'y ~ x'

corr plot for figure

max_total_read_depth <- 250*1e6
print(min_genes_gt_0)

## [1] 100

sum(counts_and_meta$genes_expressed_above_0 <= min_genes_gt_0)

## [1] 78

sum(counts_and_meta$Total_reads > max_total_read_depth)

## [1] 105

# min_genes_gt_0
exclude_for_depth <- counts_and_meta %>%
  filter(Total_reads > max_total_read_depth) %>%
  pull(dataset_id)

exclude_for_gene_count <- counts_and_meta %>%
  filter(genes_expressed_above_0 < min_genes_gt_0) %>%
  pull(dataset_id)


this_data <- expr_gene_and_read_counts_to_plot %>% 
      filter(
        Expression_threshold %in% paste0("genes_expressed_above_", 1:3),
       ! dataset_id %in% exclude_for_depth, 
        ! dataset_id %in% exclude_for_gene_count
      ) %>%
  mutate(Expression_threshold = paste(str_replace(Expression_threshold, "genes_expressed_above_", "genes > "), "TPM"))  

n_distinct(this_data$dataset_id)

## [1] 1996

this_plot_title <- paste("Correlations calculated across all but datasets with excessive read depth or low gene count")

these_stats <- this_data  %>%
group_by(Read_type, Expression_threshold) %>%
  summarize(r_corr = round(cor(Gene_count, Read_count), 2))

colors_for_correlation_plot <- c("Total_reads"="#1F78B4", "Mapped"="#FF7F00", 
"MND"="#E31A1C", "MEND"="#000000")

cor_plots_excluding_high_read_depth_and_low_gene_count_datasets <- ggplot(this_data,
       aes(x=Read_count/1E6, y=Gene_count/1e3, color = Read_type)) + 
  geom_point(alpha = 0.5, shape = 20, size =0.1) +
  geom_smooth(method = "lm", color = "black") +
  geom_label(data = these_stats, 
             aes(label=paste0("r=", r_corr)), 
             x=max(this_data$Read_count/1E6),
             y=max(this_data$Gene_count/1e3),
             hjust = 1, vjust = 1
             ) +
    theme_minimal() +
    theme(axis.line.x = element_line(color="darkgrey", size = 0.5),
        axis.line.y = element_line(color="darkgrey", size = 0.5),
        strip.text = element_text(face = "bold")) +
  scale_color_manual(values = colors_for_correlation_plot) +
  facet_grid(Read_type~Expression_threshold) +
  theme(legend.position="none") +
  scale_x_continuous("Read counts (million)", n.breaks = 4) +
  ylab("Gene counts (thousand)") 


cor_plots_excluding_high_read_depth_and_low_gene_count_datasets

## `geom_smooth()` using formula 'y ~ x'

# Figure 2

f2a <- read_type_fractions_histogram +
            theme(strip.text.x = element_text(size = 10, face = "bold"),
                  axis.title.x=element_blank(),
                  axis.text.x=element_blank())

f2b <- read_type_fractions_per_cohort_boxplot +
            theme(strip.text.x = element_blank(),
                  strip.background = element_blank(),
                  strip.text = element_blank())

f2ab <- plot_grid(f2a, 
                  f2b,
                  ncol = 1,
                  rel_heights = c(1.5, 6),
                  align = "v",
                  labels = "AUTO")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

f2c <- plot_grid(cor_plots_excluding_high_read_depth_and_low_gene_count_datasets,
          labels = "C")

## `geom_smooth()` using formula 'y ~ x'

f2 <- plot_grid(f2ab,
                 f2c,
                 nrow=1,
                 rel_widths = c(4,2.3))

figure_name <- "f2"
f2 +   draw_label(paste(figure_name, Sys.time()),
             x = 0, y = 0.02, hjust = 0, size = 6)

Plot sequence length against pct duplicates

ggplot(read_counts_with_read_type_fractions) + 
  geom_point(aes(x=seq_length, y=pct_dupe_of_mapped))

ggplot(read_counts_with_read_type_fractions) + 
  geom_boxplot(aes(x=seq_length, y=pct_dupe_of_mapped, group = seq_length))

Does the depth of sequencing correlate to the number of duplicates? How much variance does that explain?

cor_tot_reads_and_dupes <- cor(read_counts_with_read_type_fractions$Total_reads, 
    read_counts_with_read_type_fractions$pct_dupe_of_mapped,
    method = c("pearson"))
round(cor_tot_reads_and_dupes, 2)

## [1] 0.58

round(cor_tot_reads_and_dupes^2, 2)

## [1] 0.34

SessionInfo

pander(sessionInfo(), compact = FALSE)

R version 3.6.0 (2019-04-26)

Platform: x86_64-apple-darwin15.6.0 (64-bit)

locale: en_US.UTF-8||en_US.UTF-8||en_US.UTF-8||C||en_US.UTF-8||en_US.UTF-8

attached base packages:

stats
graphics
grDevices
utils
datasets
methods
base

other attached packages:

corrr(v.0.4.0)
kableExtra(v.1.1.0)
pander(v.0.6.3)
RColorBrewer(v.1.1-2)
cowplot(v.1.0.0)
ggpubr(v.0.2.5)
magrittr(v.1.5)
ggthemes(v.4.2.0)
knitr(v.1.25)
viridis(v.0.5.1)
viridisLite(v.0.3.0)
janitor(v.1.2.0)
forcats(v.0.4.0)
stringr(v.1.4.0)
dplyr(v.0.8.5)
purrr(v.0.3.3)
readr(v.1.3.1)
tidyr(v.1.0.0)
tibble(v.3.0.1)
ggplot2(v.3.3.0)
tidyverse(v.1.2.1)

loaded via a namespace (and not attached):

Rcpp(v.1.0.3)
lubridate(v.1.7.4)
lattice(v.0.20-38)
assertthat(v.0.2.1)
digest(v.0.6.23)
utf8(v.1.1.4)
R6(v.2.4.1)
cellranger(v.1.1.0)
backports(v.1.1.5)
evaluate(v.0.14)
httr(v.1.4.1)
highr(v.0.8)
pillar(v.1.4.3)
rlang(v.0.4.6)
readxl(v.1.3.1)
rstudioapi(v.0.10)
Matrix(v.1.2-17)
rmarkdown(v.1.16)
splines(v.3.6.0)
labeling(v.0.3)
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rna-seq-survey-MS-v20

hbeale

15 July, 2020

Versions

Overview

Libraries

Config

Settings

Functions

Import data

Colors

Datasets analyzed

Diseases in dataset

Cohorts in dataset

repository supp table

data access table

Genders in dataset

Ages in dataset

Read lengths in dataset

Summarize total read counts

Calculate percentages

Summarize read type fractions

statements about read type fractions

Overview

unmapped

duplicate

exonic

Correlation of depth of sequencing to duplicate read fraction

Does the depth of sequencing correlate to the number of duplicates? How much variance does that explain?

Plot correlation of depth of sequencing to duplicate read fraction

Generate schematic defining read types

Generate schematic defining read type fractions

Plot read fractions

Calculate MEND reads as a fraction of total

Show per-cohort distribution of read_type_fractions

Characterize Cohort 4

in MS

other

Expressed genes

What is the distribution of measured genes?

What are the exact lowest gene counts?

correlations with all datasets

correlations with all but low gene count datasets

Plot correlations betweeen gene counts and read types

Groom data

Identify datasets with unusual gene counts or read depths

Function for calculating gene count correlations and plotting results

for all datasets

corr plot for figure

Plot sequence length against pct duplicates

Does the depth of sequencing correlate to the number of duplicates? How much variance does that explain?

SessionInfo